Tolerance implementation

R/catch.R

@@ -7,9 +7,7 @@
 #'
 #' The underlying functionality is handled by `create_object_list()`.
 #'
-#' @param df_current data.frame, the newest/current version of dataset x.
-#' @param df_previous data.frame, the old version of dataset, for example x - t1.
-#' @param datetime_variable character, which variable to use as unique ID to join `df_current` and `df_previous`. Usually a "datetime" variable.
+#' @inheritParams create_object_list
 #'
 #' @returns A dataframe which contains only rows of `df_current` that have changes from `df_previous`, but without new rows.
 #' also returns a waldo object as in `loupe()`.
@@ -28,30 +26,40 @@
 #' df_caught
 #'
 #' @export
-catch <- function(df_current, df_previous, datetime_variable) {
+catch <- function(df_current, df_previous, datetime_variable, ...) {
   butterfly_object_list <- create_object_list(
     df_current,
     df_previous,
-    datetime_variable
+    datetime_variable,
+    ...
   )
 
-  # By using an inner join, we drop any row which does not match in
-  # df_previous.
-  df_rows_changed_from_previous <- suppressMessages(
-    dplyr::anti_join(
-      butterfly_object_list$df_current_without_new_row,
-      df_previous
+  if (butterfly_object_list$butterfly_status == TRUE) {
+    cli::cat_bullet(
+      "There are no differences, so there are no rows to return Did you specify a tolerance that exceeds number of differences?",
+      bullet = "info",
+      col = "orange",
+      bullet_col = "orange"
+    )
+  } else {
+    # By using an inner join, we drop any row which does not match in
+    # df_previous.
+    df_rows_changed_from_previous <- suppressMessages(
+      dplyr::anti_join(
+        butterfly_object_list$df_current_without_new_row,
+        df_previous
+      )
     )
-  )
 
-  cli::cat_line()
+    cli::cat_line()
 
-  cli::cat_bullet(
-    "Only these rows are returned.",
-    bullet = "info",
-    col = "orange",
-    bullet_col = "orange"
-  )
+    cli::cat_bullet(
+      "Only these rows are returned.",
+      bullet = "info",
+      col = "orange",
+      bullet_col = "orange"
+    )
 
-  return(df_rows_changed_from_previous)
+    return(df_rows_changed_from_previous)
+  }
 }

R/create_object_list.R

@@ -18,6 +18,8 @@
 #' @param df_previous data.frame, the old version of dataset, for example x - t1.
 #' @param datetime_variable string, which variable to use as unique ID to join
 #'  `df_current` and `df_previous`. Usually a "datetime" variable.
+#' @param ... Other `waldo::compare()` arguments can be supplied here,
+#'   such as `tolerance` or `max_diffs`. See `?waldo::compare()` for a full list.
 #'
 #' @returns A list containing boolean where TRUE indicates no changes to
 #' previous data and FALSE indicates unexpected changes, a dataframe of
@@ -32,8 +34,23 @@
 #'
 #' butterfly_object_list
 #'
+#' # You can pass other `waldo::compare()` options such as tolerance here
+#' butterfly_object_list <- butterfly::create_object_list(
+#'   butterflycount$march, # This is your new or current dataset
+#'   butterflycount$february, # This is the previous version you are comparing it to
+#'   datetime_variable = "time", # This is the unique ID variable they have in common
+#'   tolerance = 2
+#' )
+#'
+#' butterfly_object_list
+#'
 #' @export
-create_object_list <- function(df_current, df_previous, datetime_variable) {
+create_object_list <- function(
+    df_current,
+    df_previous,
+    datetime_variable,
+    ...
+    ) {
   # Check input is as expected
   stopifnot("`df_current` must be a data.frame" = is.data.frame(df_current))
   stopifnot("`df_previous` must be a data.frame" = is.data.frame(df_previous))
@@ -70,7 +87,8 @@ create_object_list <- function(df_current, df_previous, datetime_variable) {
   # Compare the current data with the previous data, without "new" values
   waldo_object <- waldo::compare(
     df_current_without_new_row,
-    df_previous
+    df_previous,
+    ...
   )
 
   # Creating a feedback message depending on the waldo object's output

R/loupe.R

@@ -20,9 +20,7 @@
 #'
 #' The underlying functionality is handled by `create_object_list()`.
 #'
-#' @param df_current data.frame, the newest/current version of dataset x.
-#' @param df_previous data.frame, the old version of dataset, for example x - t1.
-#' @param datetime_variable string, which variable to use as unique ID to join `df_current` and `df_previous`. Usually a "datetime" variable.
+#' @inheritParams create_object_list
 #'
 #' @returns A boolean where TRUE indicates no changes to previous data and FALSE indicates unexpected changes.
 #'
@@ -45,11 +43,12 @@
 #' )
 #'
 #' @export
-loupe <- function(df_current, df_previous, datetime_variable) {
+loupe <- function(df_current, df_previous, datetime_variable, ...) {
   butterfly_object_list <- create_object_list(
     df_current,
     df_previous,
-    datetime_variable
+    datetime_variable,
+    ...
   )
 
   return(butterfly_object_list$butterfly_status)

R/release.R

@@ -5,9 +5,7 @@
 #' which contains the new rows (if present) but matched rows which contain
 #' changes from previous data will be dropped.
 #'
-#' @param df_current data.frame, the newest/current version of dataset x.
-#' @param df_previous data.frame, the old version of dataset, for example x - t1.
-#' @param datetime_variable string, which variable to use as unique ID to join `df_current` and `df_previous`. Usually a "datetime" variable.
+#' @inheritParams create_object_list
 #' @param include_new boolean, should new rows be included? Default is TRUE.
 #'
 #' @returns A dataframe which contains only rows of `df_current` that have not changed from `df_previous`, and includes new rows.
@@ -28,52 +26,64 @@
 #' df_released
 #'
 #' @export
-release <- function(df_current, df_previous, datetime_variable, include_new = TRUE) {
+release <- function(df_current, df_previous, datetime_variable, include_new = TRUE, ...) {
   butterfly_object_list <- create_object_list(
     df_current,
     df_previous,
-    datetime_variable
+    datetime_variable,
+    ...
   )
 
-  # By using an inner join, we drop any row which does not match in
-  # df_previous.
-  df_current_without_changed_rows <- suppressMessages(
-    dplyr::inner_join(
-      butterfly_object_list$df_current_without_new_row,
-      df_previous
-    )
-  )
-
-  # Returng the dataframe with or without new rows added
-  if (include_new == TRUE) {
-    # Then we add the new rows back in and return the dataframe as such
-    df_release <- dplyr::bind_rows(
-      butterfly_object_list$df_current_new_rows,
-      df_current_without_changed_rows
-    )
-
-    cli::cat_line()
+  if (butterfly_object_list$butterfly_status == TRUE){
 
     cli::cat_bullet(
-      "These will be dropped, but new rows are included.",
+      "There are no differences, so there are no rows to drop. Did you specify a tolerance that exceeds number of differences?",
       bullet = "info",
       col = "orange",
       bullet_col = "orange"
     )
 
-    return(df_release)
+  } else {
+    # By using an inner join, we drop any row which does not match in
+    # df_previous.
+    df_current_without_changed_rows <- suppressMessages(
+      dplyr::inner_join(
+        butterfly_object_list$df_current_without_new_row,
+        df_previous
+      )
+    )
 
-  } else if (include_new == FALSE) {
-    cli::cat_line()
+    # Returng the dataframe with or without new rows added
+    if (include_new == TRUE) {
+      # Then we add the new rows back in and return the dataframe as such
+      df_release <- dplyr::bind_rows(
+        butterfly_object_list$df_current_new_rows,
+        df_current_without_changed_rows
+      )
 
-    cli::cat_bullet(
-      "These will be dropped, along with new rows.",
-      bullet = "info",
-      col = "orange",
-      bullet_col = "orange"
-    )
+      cli::cat_line()
+
+      cli::cat_bullet(
+        "These will be dropped, but new rows are included.",
+        bullet = "info",
+        col = "orange",
+        bullet_col = "orange"
+      )
+
+      return(df_release)
+
+    } else if (include_new == FALSE) {
+      cli::cat_line()
+
+      cli::cat_bullet(
+        "These will be dropped, along with new rows.",
+        bullet = "info",
+        col = "orange",
+        bullet_col = "orange"
+      )
 
-    # If new rows are not included, simply return the df without changed rows
-    return(df_current_without_changed_rows)
+      # If new rows are not included, simply return the df without changed rows
+      return(df_current_without_changed_rows)
+    }
   }
 }

tests/testthat/test-create_object_list.R

@@ -68,3 +68,17 @@ test_that("comparison object is returned when not equal", {
     0
   )
 })
+
+test_that("passing of additional waldo arguments works as expected", {
+  # Adding a tolerance of 2 should now "ignore" the single change
+  create_object_list_output <- create_object_list(
+    butterflycount$march,
+    butterflycount$february,
+    datetime_variable = "time",
+    tolerance = 2
+  )
+
+  testthat::expect_true(
+    create_object_list_output$butterfly_status
+  )
+})

vignettes/butterfly.Rmd

@@ -53,6 +53,23 @@ butterfly::loupe(
 
 `butterfly` follows the `waldo` philosophy of erring on the side of providing too much information, rather than too little. It will give a detailed feedback message on the status between two objects.
 
+### Additional arguments from `waldo::compare()`
+
+You have the flexibility to pass further arguments that `waldo::compare()` accepts, to any butterfly function, for instance to specify the tolerance.
+
+If we add a tolerance of 2 to the previous example, no differences should be returned:
+
+```{r tolerance_example}
+butterfly::loupe(
+  butterflycount$march,
+  butterflycount$february,
+  datetime_variable = "time",
+  tolerance = 2 # <- setting a tolerance of 2
+)
+```
+
+Call `?waldo::compare()` to see the full list of arguments.
+
 ## Extracting unexpected changes: catch()
 
 You might want to return changed rows as a dataframe. For this `butterfly::catch()`is provided.